Abstract
The study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. We showed that the coupling between each brain region and the rest of the cortex provides an efficient statistic to classify the two brain states. Based on this and other statistics, we estimated maximum entropy models to derive collective, macroscopic properties that quantify the system's capabilities to produce work, to contain information, and to transmit it, which were all maximized in the awake state. The differences in these properties were consistent with a phase transition from critical dynamics in the awake state to supercritical dynamics in the anesthetized state. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in the state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.
Highlights
Changes in spontaneous brain activity are observed in different brain states, the study of which is essential to understand the organizing principles of brain activity
We found that χ and Ch were significantly higher for the awake state compared to all anesthetized conditions for both linear and non-linear couplingMEM
These results suggest that the awake state displayed critical dynamics, while dynamics under anesthesia were super-critical, which indicates that the anesthetics had a disconnection effect
Summary
Interesting phenomena in biological systems are usually collective behaviors emerging from the interactions among many constituents. How changes in local regions and subnetworks combine to affect the collective brain dynamics and to lose consciousness remains largely unknown To answer this question, it is essential to precisely characterize the collective properties of different brain states and their dependence on parameters at the system’s level. The global mechanisms underlying different conscious states have been recently investigated using an anatomically-constrained dynamical model with a global coupling parameter in combination with EEG recordings [25] It remains unknown which are the macroscopic properties and the relevant local/global parameters describing the transition of collective activity from the awake to anesthetized states. We derived efficient statistics that distinguished between awake and anesthetized brain states We used these statistics and the maximum entropy principle to model the brain’s activity and to derive important emergent properties that described the different brain states. We investigated the dependence of collective activity on the different model parameters
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